{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.models as models\n",
    "import torchvision.transforms as transforms\n",
    "from torchvision.transforms import RandAugment\n",
    "from torch.optim import lr_scheduler\n",
    "import numpy as np\n",
    "import time\n",
    "import copy\n",
    "import matplotlib.pyplot as plt\n",
    "from HighwayNet import *\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n",
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)\n",
    " ## 修改模型\n",
    "net = HighwayNet(drop = 0).to(device)\n",
    "num_epochs=50\n",
    " # 定义损失函数和优化器\n",
    "trainloader,testloader = get_data_loader()\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)\n",
    "scheduler = lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.1)\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/49 轮训练\n",
      "----------\n",
      "train 损失: 4.0925 Top-1 准确率: 0.0719 Top-5 准确率: 0.2430\n",
      "val 损失: 4.1417 Top-1 准确率: 0.0778 Top-5 准确率: 0.2475\n",
      "\n",
      "第 1/49 轮训练\n",
      "----------\n",
      "train 损失: 3.4804 Top-1 准确率: 0.1638 Top-5 准确率: 0.4277\n",
      "val 损失: 3.5531 Top-1 准确率: 0.1597 Top-5 准确率: 0.4175\n",
      "\n",
      "第 2/49 轮训练\n",
      "----------\n",
      "train 损失: 2.9662 Top-1 准确率: 0.2524 Top-5 准确率: 0.5662\n",
      "val 损失: 2.9948 Top-1 准确率: 0.2432 Top-5 准确率: 0.5567\n",
      "\n",
      "第 3/49 轮训练\n",
      "----------\n",
      "train 损失: 2.6015 Top-1 准确率: 0.3253 Top-5 准确率: 0.6533\n",
      "val 损失: 2.6825 Top-1 准确率: 0.3013 Top-5 准确率: 0.6413\n",
      "\n",
      "第 4/49 轮训练\n",
      "----------\n",
      "train 损失: 2.3274 Top-1 准确率: 0.3810 Top-5 准确率: 0.7137\n",
      "val 损失: 2.2730 Top-1 准确率: 0.3955 Top-5 准确率: 0.7168\n",
      "\n",
      "第 5/49 轮训练\n",
      "----------\n",
      "train 损失: 2.1136 Top-1 准确率: 0.4315 Top-5 准确率: 0.7565\n",
      "val 损失: 2.9665 Top-1 准确率: 0.2936 Top-5 准确率: 0.6295\n",
      "\n",
      "第 6/49 轮训练\n",
      "----------\n",
      "train 损失: 1.9514 Top-1 准确率: 0.4703 Top-5 准确率: 0.7857\n",
      "val 损失: 2.1765 Top-1 准确率: 0.4250 Top-5 准确率: 0.7375\n",
      "\n",
      "第 7/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8226 Top-1 准确率: 0.5019 Top-5 准确率: 0.8092\n",
      "val 损失: 2.0935 Top-1 准确率: 0.4364 Top-5 准确率: 0.7594\n",
      "\n",
      "第 8/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7122 Top-1 准确率: 0.5271 Top-5 准确率: 0.8288\n",
      "val 损失: 1.9555 Top-1 准确率: 0.4816 Top-5 准确率: 0.7817\n",
      "\n",
      "第 9/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6233 Top-1 准确率: 0.5491 Top-5 准确率: 0.8416\n",
      "val 损失: 1.9118 Top-1 准确率: 0.4906 Top-5 准确率: 0.7916\n",
      "\n",
      "第 10/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5396 Top-1 准确率: 0.5687 Top-5 准确率: 0.8550\n",
      "val 损失: 1.9880 Top-1 准确率: 0.4840 Top-5 准确率: 0.7802\n",
      "\n",
      "第 11/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4671 Top-1 准确率: 0.5862 Top-5 准确率: 0.8687\n",
      "val 损失: 1.6726 Top-1 准确率: 0.5364 Top-5 准确率: 0.8341\n",
      "\n",
      "第 12/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3988 Top-1 准确率: 0.6041 Top-5 准确率: 0.8777\n",
      "val 损失: 1.9718 Top-1 准确率: 0.4885 Top-5 准确率: 0.7915\n",
      "\n",
      "第 13/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3389 Top-1 准确率: 0.6221 Top-5 准确率: 0.8866\n",
      "val 损失: 1.5749 Top-1 准确率: 0.5665 Top-5 准确率: 0.8488\n",
      "\n",
      "第 14/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2874 Top-1 准确率: 0.6349 Top-5 准确率: 0.8922\n",
      "val 损失: 1.6011 Top-1 准确率: 0.5541 Top-5 准确率: 0.8533\n",
      "\n",
      "第 15/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2405 Top-1 准确率: 0.6457 Top-5 准确率: 0.8994\n",
      "val 损失: 1.5023 Top-1 准确率: 0.5802 Top-5 准确率: 0.8630\n",
      "\n",
      "第 16/49 轮训练\n",
      "----------\n",
      "train 损失: 1.1933 Top-1 准确率: 0.6589 Top-5 准确率: 0.9061\n",
      "val 损失: 1.5184 Top-1 准确率: 0.5856 Top-5 准确率: 0.8634\n",
      "\n",
      "第 17/49 轮训练\n",
      "----------\n",
      "train 损失: 1.1555 Top-1 准确率: 0.6669 Top-5 准确率: 0.9117\n",
      "val 损失: 1.5552 Top-1 准确率: 0.5760 Top-5 准确率: 0.8526\n",
      "\n",
      "第 18/49 轮训练\n",
      "----------\n",
      "train 损失: 1.1122 Top-1 准确率: 0.6777 Top-5 准确率: 0.9182\n",
      "val 损失: 1.5499 Top-1 准确率: 0.5767 Top-5 准确率: 0.8538\n",
      "\n",
      "第 19/49 轮训练\n",
      "----------\n",
      "train 损失: 1.0706 Top-1 准确率: 0.6913 Top-5 准确率: 0.9233\n",
      "val 损失: 1.8443 Top-1 准确率: 0.5298 Top-5 准确率: 0.8140\n",
      "\n",
      "第 20/49 轮训练\n",
      "----------\n",
      "train 损失: 1.0314 Top-1 准确率: 0.7020 Top-5 准确率: 0.9271\n",
      "val 损失: 1.5124 Top-1 准确率: 0.5884 Top-5 准确率: 0.8586\n",
      "\n",
      "第 21/49 轮训练\n",
      "----------\n",
      "train 损失: 0.9973 Top-1 准确率: 0.7106 Top-5 准确率: 0.9324\n",
      "val 损失: 1.4038 Top-1 准确率: 0.6153 Top-5 准确率: 0.8763\n",
      "\n",
      "第 22/49 轮训练\n",
      "----------\n",
      "train 损失: 0.9694 Top-1 准确率: 0.7185 Top-5 准确率: 0.9347\n",
      "val 损失: 1.4957 Top-1 准确率: 0.5905 Top-5 准确率: 0.8646\n",
      "\n",
      "第 23/49 轮训练\n",
      "----------\n",
      "train 损失: 0.9363 Top-1 准确率: 0.7272 Top-5 准确率: 0.9400\n",
      "val 损失: 1.4651 Top-1 准确率: 0.6043 Top-5 准确率: 0.8717\n",
      "\n",
      "第 24/49 轮训练\n",
      "----------\n",
      "train 损失: 0.9045 Top-1 准确率: 0.7361 Top-5 准确率: 0.9421\n",
      "val 损失: 1.4238 Top-1 准确率: 0.6238 Top-5 准确率: 0.8804\n",
      "\n",
      "第 25/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8806 Top-1 准确率: 0.7428 Top-5 准确率: 0.9443\n",
      "val 损失: 1.4264 Top-1 准确率: 0.6223 Top-5 准确率: 0.8734\n",
      "\n",
      "第 26/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8402 Top-1 准确率: 0.7533 Top-5 准确率: 0.9501\n",
      "val 损失: 1.3967 Top-1 准确率: 0.6211 Top-5 准确率: 0.8817\n",
      "\n",
      "第 27/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8151 Top-1 准确率: 0.7623 Top-5 准确率: 0.9524\n",
      "val 损失: 1.3224 Top-1 准确率: 0.6360 Top-5 准确率: 0.8857\n",
      "\n",
      "第 28/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7934 Top-1 准确率: 0.7650 Top-5 准确率: 0.9542\n",
      "val 损失: 1.3623 Top-1 准确率: 0.6371 Top-5 准确率: 0.8852\n",
      "\n",
      "第 29/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7715 Top-1 准确率: 0.7716 Top-5 准确率: 0.9561\n",
      "val 损失: 1.6057 Top-1 准确率: 0.5950 Top-5 准确率: 0.8657\n",
      "\n",
      "第 30/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7369 Top-1 准确率: 0.7817 Top-5 准确率: 0.9602\n",
      "val 损失: 1.4198 Top-1 准确率: 0.6234 Top-5 准确率: 0.8751\n",
      "\n",
      "第 31/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7213 Top-1 准确率: 0.7848 Top-5 准确率: 0.9624\n",
      "val 损失: 1.2363 Top-1 准确率: 0.6603 Top-5 准确率: 0.9020\n",
      "\n",
      "第 32/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6947 Top-1 准确率: 0.7956 Top-5 准确率: 0.9635\n",
      "val 损失: 1.3855 Top-1 准确率: 0.6367 Top-5 准确率: 0.8879\n",
      "\n",
      "第 33/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6701 Top-1 准确率: 0.8015 Top-5 准确率: 0.9672\n",
      "val 损失: 1.3853 Top-1 准确率: 0.6336 Top-5 准确率: 0.8794\n",
      "\n",
      "第 34/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6597 Top-1 准确率: 0.8063 Top-5 准确率: 0.9664\n",
      "val 损失: 1.3167 Top-1 准确率: 0.6481 Top-5 准确率: 0.8941\n",
      "\n",
      "第 35/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6271 Top-1 准确率: 0.8144 Top-5 准确率: 0.9712\n",
      "val 损失: 1.2329 Top-1 准确率: 0.6672 Top-5 准确率: 0.9071\n",
      "\n",
      "第 36/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6108 Top-1 准确率: 0.8191 Top-5 准确率: 0.9725\n",
      "val 损失: 1.2405 Top-1 准确率: 0.6708 Top-5 准确率: 0.9034\n",
      "\n",
      "第 37/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5952 Top-1 准确率: 0.8248 Top-5 准确率: 0.9730\n",
      "val 损失: 1.3734 Top-1 准确率: 0.6556 Top-5 准确率: 0.8925\n",
      "\n",
      "第 38/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5713 Top-1 准确率: 0.8314 Top-5 准确率: 0.9753\n",
      "val 损失: 1.3525 Top-1 准确率: 0.6487 Top-5 准确率: 0.8930\n",
      "\n",
      "第 39/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5587 Top-1 准确率: 0.8349 Top-5 准确率: 0.9758\n",
      "val 损失: 1.2943 Top-1 准确率: 0.6661 Top-5 准确率: 0.8972\n",
      "\n",
      "第 40/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5432 Top-1 准确率: 0.8387 Top-5 准确率: 0.9779\n",
      "val 损失: 1.2554 Top-1 准确率: 0.6739 Top-5 准确率: 0.9009\n",
      "\n",
      "第 41/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5214 Top-1 准确率: 0.8453 Top-5 准确率: 0.9788\n",
      "val 损失: 1.3023 Top-1 准确率: 0.6641 Top-5 准确率: 0.8978\n",
      "\n",
      "第 42/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5068 Top-1 准确率: 0.8509 Top-5 准确率: 0.9809\n",
      "val 损失: 1.3980 Top-1 准确率: 0.6410 Top-5 准确率: 0.8925\n",
      "\n",
      "第 43/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4902 Top-1 准确率: 0.8553 Top-5 准确率: 0.9818\n",
      "val 损失: 1.3297 Top-1 准确率: 0.6645 Top-5 准确率: 0.8935\n",
      "\n",
      "第 44/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4782 Top-1 准确率: 0.8589 Top-5 准确率: 0.9827\n",
      "val 损失: 1.3953 Top-1 准确率: 0.6577 Top-5 准确率: 0.8951\n",
      "\n",
      "第 45/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4664 Top-1 准确率: 0.8638 Top-5 准确率: 0.9838\n",
      "val 损失: 1.4186 Top-1 准确率: 0.6474 Top-5 准确率: 0.8882\n",
      "\n",
      "第 46/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4581 Top-1 准确率: 0.8642 Top-5 准确率: 0.9838\n",
      "val 损失: 1.3402 Top-1 准确率: 0.6624 Top-5 准确率: 0.8938\n",
      "\n",
      "第 47/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4488 Top-1 准确率: 0.8675 Top-5 准确率: 0.9841\n",
      "val 损失: 1.4230 Top-1 准确率: 0.6588 Top-5 准确率: 0.8951\n",
      "\n",
      "第 48/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4359 Top-1 准确率: 0.8731 Top-5 准确率: 0.9852\n",
      "val 损失: 1.2564 Top-1 准确率: 0.6877 Top-5 准确率: 0.9034\n",
      "\n",
      "第 49/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4197 Top-1 准确率: 0.8772 Top-5 准确率: 0.9864\n",
      "val 损失: 1.2817 Top-1 准确率: 0.6782 Top-5 准确率: 0.8978\n",
      "\n",
      "训练完成，耗时 98m 42s\n",
      "最佳验证集准确率: 0.687700\n"
     ]
    }
   ],
   "source": [
    "model,(train_losses, train_top1_accs, train_top5_accs, val_losses, val_top1_accs, val_top5_accs) = train_model(net,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_cpu_numpy(tensor):\n",
    "    return tensor.cpu().numpy()\n",
    "def to_cpu(list_):\n",
    "    if isinstance(list_, list):\n",
    "        return list(map(to_cpu_numpy,list_))\n",
    "    else:\n",
    "        return to_cpu_numpy(list_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 39564 (\\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 35777 (\\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
      "  \n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 39564 (\\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 35777 (\\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training_results(train_losses, to_cpu(train_top1_accs), to_cpu(train_top5_accs), val_losses, to_cpu(val_top1_accs), to_cpu(val_top5_accs))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/9 轮训练\n",
      "----------\n",
      "train 损失: 0.2664 Top-1 准确率: 0.9303 Top-5 准确率: 0.9934\n",
      "val 损失: 1.0046 Top-1 准确率: 0.7395 Top-5 准确率: 0.9300\n",
      "\n",
      "第 1/9 轮训练\n",
      "----------\n",
      "train 损失: 0.2139 Top-1 准确率: 0.9466 Top-5 准确率: 0.9946\n",
      "val 损失: 0.9853 Top-1 准确率: 0.7440 Top-5 准确率: 0.9317\n",
      "\n",
      "第 2/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1954 Top-1 准确率: 0.9525 Top-5 准确率: 0.9959\n",
      "val 损失: 0.9980 Top-1 准确率: 0.7400 Top-5 准确率: 0.9313\n",
      "\n",
      "第 3/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1844 Top-1 准确率: 0.9573 Top-5 准确率: 0.9963\n",
      "val 损失: 0.9941 Top-1 准确率: 0.7434 Top-5 准确率: 0.9330\n",
      "\n",
      "第 4/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1753 Top-1 准确率: 0.9587 Top-5 准确率: 0.9966\n",
      "val 损失: 0.9790 Top-1 准确率: 0.7461 Top-5 准确率: 0.9325\n",
      "\n",
      "第 5/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1670 Top-1 准确率: 0.9610 Top-5 准确率: 0.9970\n",
      "val 损失: 0.9825 Top-1 准确率: 0.7446 Top-5 准确率: 0.9326\n",
      "\n",
      "第 6/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1640 Top-1 准确率: 0.9623 Top-5 准确率: 0.9964\n",
      "val 损失: 0.9719 Top-1 准确率: 0.7468 Top-5 准确率: 0.9308\n",
      "\n",
      "第 7/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1581 Top-1 准确率: 0.9643 Top-5 准确率: 0.9968\n",
      "val 损失: 0.9619 Top-1 准确率: 0.7503 Top-5 准确率: 0.9337\n",
      "\n",
      "第 8/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1527 Top-1 准确率: 0.9652 Top-5 准确率: 0.9970\n",
      "val 损失: 0.9710 Top-1 准确率: 0.7474 Top-5 准确率: 0.9318\n",
      "\n",
      "第 9/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1474 Top-1 准确率: 0.9672 Top-5 准确率: 0.9972\n",
      "val 损失: 0.9814 Top-1 准确率: 0.7449 Top-5 准确率: 0.9311\n",
      "\n",
      "训练完成，耗时 20m 11s\n",
      "最佳验证集准确率: 0.750300\n"
     ]
    }
   ],
   "source": [
    "model2,(train_losses2, train_top1_accs2, train_top5_accs2, val_losses2, val_top1_accs2, val_top5_accs2) = train_model(net,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs = 10)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/29 轮训练\n",
      "----------\n",
      "train 损失: 0.1527 Top-1 准确率: 0.9653 Top-5 准确率: 0.9969\n",
      "val 损失: 0.9727 Top-1 准确率: 0.7478 Top-5 准确率: 0.9325\n",
      "\n",
      "第 1/29 轮训练\n",
      "----------\n",
      "train 损失: 0.1438 Top-1 准确率: 0.9678 Top-5 准确率: 0.9975\n",
      "val 损失: 0.9724 Top-1 准确率: 0.7480 Top-5 准确率: 0.9327\n",
      "\n",
      "第 2/29 轮训练\n",
      "----------\n",
      "train 损失: 0.1442 Top-1 准确率: 0.9676 Top-5 准确率: 0.9975\n",
      "val 损失: 0.9545 Top-1 准确率: 0.7534 Top-5 准确率: 0.9336\n",
      "\n",
      "第 3/29 轮训练\n",
      "----------\n",
      "train 损失: 0.1428 Top-1 准确率: 0.9679 Top-5 准确率: 0.9971\n",
      "val 损失: 0.9727 Top-1 准确率: 0.7491 Top-5 准确率: 0.9339\n",
      "\n",
      "第 4/29 轮训练\n",
      "----------\n",
      "train 损失: 0.1356 Top-1 准确率: 0.9705 Top-5 准确率: 0.9977\n",
      "val 损失: 0.9730 Top-1 准确率: 0.7494 Top-5 准确率: 0.9332\n",
      "\n",
      "第 5/29 轮训练\n",
      "----------\n",
      "train 损失: 0.1351 Top-1 准确率: 0.9710 Top-5 准确率: 0.9974\n",
      "val 损失: 0.9728 Top-1 准确率: 0.7499 Top-5 准确率: 0.9328\n",
      "\n",
      "第 6/29 轮训练\n",
      "----------\n",
      "train 损失: 0.1297 Top-1 准确率: 0.9718 Top-5 准确率: 0.9976\n",
      "val 损失: 0.9715 Top-1 准确率: 0.7495 Top-5 准确率: 0.9322\n",
      "\n",
      "第 7/29 轮训练\n",
      "----------\n",
      "train 损失: 0.1280 Top-1 准确率: 0.9728 Top-5 准确率: 0.9977\n",
      "val 损失: 0.9850 Top-1 准确率: 0.7466 Top-5 准确率: 0.9329\n",
      "\n",
      "第 8/29 轮训练\n",
      "----------\n",
      "train 损失: 0.1268 Top-1 准确率: 0.9723 Top-5 准确率: 0.9980\n",
      "val 损失: 0.9650 Top-1 准确率: 0.7513 Top-5 准确率: 0.9341\n",
      "\n",
      "第 9/29 轮训练\n",
      "----------\n",
      "train 损失: 0.1248 Top-1 准确率: 0.9734 Top-5 准确率: 0.9982\n",
      "val 损失: 0.9692 Top-1 准确率: 0.7509 Top-5 准确率: 0.9335\n",
      "\n",
      "第 10/29 轮训练\n",
      "----------\n",
      "train 损失: 0.1252 Top-1 准确率: 0.9728 Top-5 准确率: 0.9978\n",
      "val 损失: 0.9774 Top-1 准确率: 0.7512 Top-5 准确率: 0.9315\n",
      "\n",
      "第 11/29 轮训练\n",
      "----------\n",
      "train 损失: 0.1199 Top-1 准确率: 0.9744 Top-5 准确率: 0.9981\n",
      "val 损失: 0.9813 Top-1 准确率: 0.7495 Top-5 准确率: 0.9320\n",
      "\n",
      "第 12/29 轮训练\n",
      "----------\n",
      "train 损失: 0.1188 Top-1 准确率: 0.9732 Top-5 准确率: 0.9981\n",
      "val 损失: 0.9747 Top-1 准确率: 0.7518 Top-5 准确率: 0.9319\n",
      "\n",
      "第 13/29 轮训练\n",
      "----------\n",
      "train 损失: 0.1152 Top-1 准确率: 0.9760 Top-5 准确率: 0.9980\n",
      "val 损失: 0.9633 Top-1 准确率: 0.7533 Top-5 准确率: 0.9357\n",
      "\n",
      "第 14/29 轮训练\n",
      "----------\n",
      "train 损失: 0.1150 Top-1 准确率: 0.9757 Top-5 准确率: 0.9978\n",
      "val 损失: 0.9843 Top-1 准确率: 0.7490 Top-5 准确率: 0.9320\n",
      "\n",
      "第 15/29 轮训练\n",
      "----------\n",
      "train 损失: 0.1092 Top-1 准确率: 0.9781 Top-5 准确率: 0.9981\n",
      "val 损失: 0.9742 Top-1 准确率: 0.7495 Top-5 准确率: 0.9331\n",
      "\n",
      "第 17/29 轮训练\n",
      "----------\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m model3,(train_losses3, train_top1_accs3, train_top5_accs3, val_losses3, val_top1_accs3, val_top5_accs3) \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel2\u001b[49m\u001b[43m,\u001b[49m\u001b[43mtrainloader\u001b[49m\u001b[43m,\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43mtestloader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscheduler\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_epochs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m30\u001b[39;49m\u001b[43m)\u001b[49m  \n",
      "File \u001b[0;32m~/deep-learning/FuTong/发起总攻/utils.py:163\u001b[0m, in \u001b[0;36mtrain_model\u001b[0;34m(model, trainloader, device, testloader, criterion, optimizer, scheduler, num_epochs)\u001b[0m\n\u001b[1;32m    160\u001b[0m         optimizer\u001b[38;5;241m.\u001b[39mstep()\n\u001b[1;32m    162\u001b[0m \u001b[38;5;66;03m# 统计\u001b[39;00m\n\u001b[0;32m--> 163\u001b[0m running_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m*\u001b[39m inputs\u001b[38;5;241m.\u001b[39msize(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m    164\u001b[0m running_corrects \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m top1_correct\n\u001b[1;32m    165\u001b[0m running_top1_corrects \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m top1_correct\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model3,(train_losses3, train_top1_accs3, train_top5_accs3, val_losses3, val_top1_accs3, val_top5_accs3) = train_model(model2,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs = 30)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model3, '90epoch_highway+02drop.pth')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里的隐藏层是1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
